Multi-bed elastic motion correction

ABSTRACT

A set of first modality data (e.g., MR or CT) is provided. The set of first modality data comprises a plurality of mu-maps, a plurality of motion vectors and a plurality of gated data. Each of the mu-maps corresponds to one of the beds. A set of second modality data (e.g., PET/SPECT) is provided. The set of second modality data comprises a plurality of frames for each of the beds. Each of the plurality of frames is warped by one or more motion vectors of the plurality of motion vectors. A single-bed image is generated for each bed by summing the frames corresponding to the bed. A whole body image is generated by summing the single-bed images for each of the beds.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority under 35 U.S.C. §119(e) to U.S.Provisional Application Ser. No. 61/979,133 filed Apr. 14, 2014, theentirety of which is incorporated by reference herein.

FIELD

Aspects of the present disclosure relate in general to processing datafor medical imaging, and more particularly to techniques for motioncorrect in multi-bed medical image processing and generation.

BACKGROUND

Multi-modality imaging systems perform diagnostic scans using multiplemodalities, such as, for example, magnetic resonance (MR/MRI), computedtomography (CT), positron emission tomography (PET), and/or singlephoton emission computed tomography (SPECT). Multiple modalities arecombined to provide complimentary and/or overlapping data sets. Forexample, MR scanning generally provides soft tissue morphological dataand provides greater resolution of structural and functionalcharacteristics of soft tissue, etc. PET scanning generally has a lowerresolution but provides more useful information regarding the functionalcondition of the body tissues and systems such as the cardiovascularsystem. PET scanning is superior for indicating the presence of tumorsor decreased blood flow to certain organs or areas of the body. Thecomplementary strengths of two or more imaging modalities can beprovided simultaneously by performing both methods in a single apparatusand imaging session.

During operation, image quality of one or more imaging modalities, suchas a PET modality, can be affected by motion during imaging, forexample, respiratory motion. When using a PET modality, imagingartifacts may be generated during image acquisition because of therespiratory motion. In multi-modality systems, the PET modality requiresa relatively long duration data acquisition period, on the order ofseveral minutes (e.g., about 2 to 30 minutes per image) for a typicalclinically sufficient image. Typically, a large number of PET dataacquisitions (e.g., frames) are acquired at many different time pointsduring this period. Consequently, patient movement is a problem in PETscanning.

PET scanning has a limited field of view (FOV) and cannot capture wholebody images. In order to perform whole body imaging, multiple PET imagesare captured at multiple positions with respect to a patient (e.g,beds). When stitching together multiple beds to form a single whole bodyPET image, motion effects and attenuation are most pronounced at theedges of the FOV (e.g., the edge voxels/slices). In multi-bed studies,breathing patterns of the patient can change between beds. Therefore,detection and compensation for the varying respiratory patterns isimportant for whole body PET reconstruction.

Single bed elastic motion correction algorithms are increasingly beingused to model and compensate for respiratory motion in clinical PETimages. If motion effects are not properly accounted for, imagenon-uniformity and incorrect quantification will occur. Although singlebed elastic motion correction has been applied, motion correction formulti-bed PET data has remained challenging.

SUMMARY

In some embodiments of the present disclosure, a method of processingdata for medical imaging is disclosed. The method comprises providing afirst set of first modality data including a first mu-map, a firstplurality of gated data, and a first plurality of motion vectors. Thefirst set of first modality data is generated by a first imagingmodality of an imaging system. A first plurality of attenuation maps isgenerated from the first set of first modality data. Each of the firstplurality of attenuation maps corresponds to a gate in the firstplurality of gated data. A first set of second modality data is providedincluding a first plurality of frames. Each of the first plurality offrames corresponds to one of the first plurality of attenuationcorrection maps. The first set of second modality data is generated by asecond imaging modality of an imaging system. The first plurality offrames are warped by corresponding motion vectors from the firstplurality of motion vectors. The first plurality of warped frames arecombined into a first single-bed image.

In some embodiments of the present disclosure, a non-transitory,computer readable medium storing computer executable instructions isdisclosed. The computer executable instructions cause a computer toreceive a first set of first modality data including a first mu-map anda first plurality of gated data, and a first plurality of motionvectors. The first set of first modality data is generated by a firstimaging modality of an imaging system. The computer generates a firstplurality of attenuation maps from the first set of first modality data.Each of the first plurality of attenuation maps corresponds to a gate inthe first plurality of gated data. A first set of second modality datais received including a first plurality of frames. Each of the firstplurality of frames corresponds to one of the first plurality ofattenuation correction maps. The first set of second modality data isgenerated by a second imaging modality of an imaging system. Thecomputer warps the first plurality of attenuation maps by correspondingmotion vectors from the first plurality of motion vectors and combinesthe first plurality of warped frames into a first single-bed image.

In some embodiments of the present disclosure, a system for medicalimaging is disclosed. The system comprises a first imaging modality, asecond imaging modality, and a computer in data communication with thefirst imaging modality and the second imaging modality. The computer isconfigured to process data for medical imaging by receiving a first setof first modality data including a first mu-map, a first plurality ofgated data from the first imaging modality, and a first plurality ofmotion vectors. The computer generates a first plurality of attenuationmaps from the first set of first modality data. Each of the firstplurality of attenuation maps corresponds to a gate in the firstplurality of gated data. A first set of second modality data is receivedfrom the second imaging modality. The first set of second modality dataincludes a first plurality of frames. Each of the first plurality offrames corresponds to one of the first plurality of attenuationcorrection maps. The computer warps the first plurality of attenuationcorrection maps by corresponding motion vectors from the first pluralityof motion vectors and combines the first plurality of warped frames intoa first single-bed image.

BRIEF DESCRIPTION OF THE DRAWINGS

The following will be apparent from elements of the figures, which areprovided for illustrative purposes and are not necessarily to scale.

FIG. 1 illustrates one embodiment of a multi-modality imaging apparatus.

FIG. 2A illustrates one embodiment of a single-bed PET-based mu-map.

FIG. 2B illustrates one embodiment of an elongated MR based mu-map.

FIG. 2C illustrates one embodiment of an elongated MR-based motionvector map overlayed on a mu-map having the same dimensions as thePET-based mu-map of FIG. 2A.

FIG. 3 illustrates one embodiment of a method for multi-bed motioncorrected reconstruction.

FIG. 4A illustrates one embodiment of a bed-by-bed reconstructed image.

FIG. 4B illustrates one embodiment of a motion corrected whole bodyreconstructed image according to the methods described herein.

FIG. 5A illustrates one embodiment of a non-uniform rational B-spline(NURBS)-based cardiac-torso (NCAT) phantom.

FIG. 5B illustrates one embodiment of a whole body image generated usinga single bed based motion field.

FIG. 5C illustrates one embodiment of a whole body reconstructed imagegenerated according to the methods described herein.

FIG. 6 illustrates one embodiment of a schematic representation of anMR-PET workflow for generating a motion corrected whole body image.

FIG. 7 is a block diagram of one embodiment of a computer systemconfigured to execute one or more steps of the methods described herein.

DETAILED DESCRIPTION

This description of the exemplary embodiments is intended to be read inconnection with the accompanying drawings, which are to be consideredpart of the entire written description.

Various embodiments of the present disclosure address the foregoingchallenges associated with motion correction for whole body combinedimaging, for example, by utilizing a new motion correction algorithmthat incorporates the motion vectors from multiple beds during wholebody assembly.

FIG. 1 shows one example of a multi-modality imaging apparatus 100 (suchas, for example, a combination MR/PET apparatus). The multi-modalityimaging apparatus 100 may be configured for two or more imagingmodalities, such as, for example, combined PET/MR, PET/CT, SPECT/MR,SPECT/CT, and/or any other suitable combined diagnostic imagingmodalities. The multi-modality imaging apparatus 100 includes a scannerfor at least a first imaging modality 112 provided in a gantry 116 a anda scanner for a second imaging modality 114 provided in a second gantry116 b. In various embodiments, PET and MR are described as examples offirst and second imaging modalities that may be used in variousembodiments, but they are non-limiting examples. A patient 117 lies on amovable patient bed 118 that may be movable between the gantries.Alternatively, the two imaging modalities 112 and 114 may be combinedtogether in a single gantry.

Scan data from at least the first and second imaging modalities 112, 114are stored at one or more computer databases 140 and processed by one ormore computer processors 150 of a computer 130. Scan data from the firstand second imaging modalities may be stored in the same database 140 orin separate databases. The graphical depiction of computer 130 in FIG. 1is provided by way of illustration only, and computer 130 may includeone or more separate computing devices. In some embodiments, thecomputer 130 is configured to generate a whole body reconstructed imagefrom a first modality data set and a second modality data set. The firstand second modality data sets can be provided by the first imagingmodality 112 and the second imaging modality 114 and/or may be providedas a separate data set, such as, for example, from memory coupled to thecomputer 130.

In some embodiments, the first and second imaging modalities 112, 114are MR and PET, respectively. For example, a patient can be scanned withthe first imaging modality 112 and the second imaging modality 114 toyield MR 3D morphological data and PET acquisition and physiologicalwaveform data, respectively. The scans may be performed sequentially,with a PET scan following a MR scan, and/or simultaneously. In anotherembodiment, the first imaging modality is PET and the second imagingmodality is MR.

In some embodiments, gating is performed based on an acquiredphysiological signals to determine gate locations (in time) and a width(in time duration) for the gates. Any gating algorithm known in the artcan be used for this purpose. Gate width (the time duration of a gate)depends on the imaging modality. The widths (time durations) ofrespective gates in a cycle may be constant or may vary, e.g., dependingon the gating algorithm that is used and the constraints of the imagingmodality.

Although combined MR and PET data is discussed herein, it will berecognize that the disclosed systems and methods are applicable to anycombined modalities, such as, for example, MR/PET, CT/PET, MR/SPECT,and/or CT/SPECT.

In some embodiments, the first and second imaging modalities 112, 114each comprise a FOV. The FOV determines a width of an image obtainableby the first or second imaging modality 112, 114. In some embodiments,the FOV of the first imaging modality 112 is greater (e.g., longer) thanthe FOV for the second imaging modality 114. For example, in someembodiments, the first FOV has a greater length with respect toattenuation and/or motion vectors. In some embodiments, the firstimaging modality 112 is an MR scan with a FOV of about 45 cm and thesecond imaging modality 114 is a PET scan with a FOV of less than 45 cm,such as, for example, less than about 25 cm, less than about 16 cm,and/or any FOV less than the FOV of the first imaging modality 112. Insome embodiments, the FOV of the imaging modality is less than totalarea to be imaged. For example, in some embodiments, the second imagingmodality is a PET imaging modality having a FOV of about 25 cm. In orderto generate a whole body image, multiple beds (e.g., imaging positions)are acquired for at least the second imaging modality 114 and stitchedtogether to generate the whole body image.

In some embodiments, the greater FOV of the first imaging modality 112is used to compensate for attenuation and/or motion at the edge slicesof the second imaging modality 114. The first imaging modality 112includes a larger FOV than the second imaging modality 114 and isconfigured to capture one or more elongated (or expanded) parameters,such as, for example, an elongated mu-map, elongated motion vectors, anexpanded sensitivity term, and/or any other suitable elongated orexpanded parameters. In some embodiments, the elongated parameters arederived from the first imaging modality data and/or the second imagingmodality data. The elongated parameters generated by the first imagingmodality 112 are used for motion correction of the second imagingmodality 114.

For example, FIG. 2A illustrates one embodiment of a PET mu-map 200having a FOV of 25 cm. The edges of the PET mu-map 200 would overlapwith the edges of subsequent PET mu-maps captured for subsequent beds.Breathing patterns of a patient can change between beds, resulting inartifacts during a whole body reconstruction process due to mismatchedmotion vectors in the PET mu-map 200 a and 3D sensitivity terms of thePET imaging modality. By utilizing a mu-map, motion vectors, 3Dsensitivity term, and/or a reconstruction volume with a longer FOVduring reconstruction, the artifacts generated during whole bodyreconstruction are reduced.

FIG. 2B illustrates one embodiment of an elongated MR-based motionvector map 200 b overlaid on a mu-map having the same dimensions as thePET-based mu-map 200 of FIG. 2A. In. The greater FOV 202 b of theMR-derived motion vector map 200 c allows the motion vectors for theedges of the PET data 200 a to be easily determined. The motion vectorinformation of the larger MR-derived mu-map 200 b is used to compensatefor motion and attenuation in the PET data 200 a during whole body imagereconstruction.

FIG. 2C illustrates a longer MR-based mu map 200 c. The MR-based mu-map200 c has a FOV 202 b greater than the FOV of the PET-based mu-map 200a, such as, for example, 33 cm. Motion vectors for the edge slices ofthe PET-based mu-map 200 a can be derived from the MR-based gated images200 c. In some embodiments, the longer mu-map and motion vectors from anMR imaging modality is used to reconstruct a motion corrected single bedPET image.

FIG. 3 illustrates one embodiment of a method 300 for generating a wholebody, multi-bed, elastic motion corrected image from a multi-bed scan.In a first step 302, dual-modality data, such as, for example, gated MRdata and PET data, is provided to the computer 130. In some embodiments,the dual-modality data is acquired by a first imaging modality 112 and asecond imaging modality 114 of a multi-modality imaging apparatus 100.The dual modality data may be acquired sequentially and/orsimultaneously. In some embodiments, the dual-modality data includespre-captured data provided to the computer 130, by, for example, amemory unit coupled to the computer 130. The dual-modality data includestwo or more beds. For example, in one embodiment, a self-gated radialVIBE MRI sequence is used to generate gated MR images for a first bedand a second bed. PET data is acquired as list-mode PET data for boththe first bed and the second bed simultaneously with the acquisition ofthe MR data. In some embodiments, the first modality data includes anelongated mu-map generated prior to, simultaneously with, and/orfollowing acquisition of the dual-modality data.

In a second step 304, motion vectors are calculated from the firstmodality data for each frame of a first bed. In some embodiments, thefirst modality data is binned (e.g., gated) and reconstructed intodiscrete states of a motion cycle, such as, for example, a respiratorycycle, for each bed. Motion vectors are calculated for each frame of thegated data. The motion vectors may be calculated using imageregistration based on one or more algorithms, such as diffeomorphicdemons algorithm. In some embodiments, the first modality data comprisesgated MR images. The motion vectors of the MR images are derived bymeans of post-processing and registration of the high resolution MRimages to the reference gate of each bed. In some embodiments, motionvectors may be calculated from the first modality data, the secondmodality data, and/or jointly estimated using both the first modalitydata and the second modality data.

In some embodiments, the second modality data, for example, list-modePET data, is divided into predetermined frames based on the amplitude ofthe motion waveform for the current bed (e.g., the discrete binsgenerated for the first modality data). In a third step 306, anattenuation correction map (mu-map) is generated for each frame of thebed from the first modality data. The attenuation correction map isgenerated by warping the first modality mu-map, for example, anelongated MR-based mu-map, with the derived motion vectors for thespecific frame. FIG. 2C illustrates one embodiment of an elongatedMR-based mu-map 200 c. The longer FOV 202 b of the MR-based mu-map 200 cenables accurate modeling of the motion vectors from adjacent beds,which are used as weighting terms when stitching multiple images of thesecond image modality together (e.g., combing a first PET bed image witha second PET bed image). The elongated motion vectors and/or attenuationcorrection maps (e.g., bed-by-bed mu-maps) of the first imaging modality112 eliminate attenuation mismatch at the edge of each bed of the secondimaging modality 114 caused by motion, such as, respiratory motion. Insome embodiments, the length of the frames for the various datasets fromthe first and second imaging modalities 112, 114 can be longer thanrequired for a single bed position. The elongated length enables usingadditional information from one or more slices/voxels outside the singlebed FOV so as to reduce artifacts in the edge slices/voxels. If theadditional slices are not available from one modality, for example thesecond imaging modality 114, estimates of the missing region may becalculated using data from the other imaging modality, for example, thefirst imaging modality 112. In some embodiments, a truncated partestimate may be calculated using an image, mu-maps and/or motionvectors.

Referring back to FIG. 3, in a fourth step 308, a norm for each gate isdetermined. In a fifth step 310, each gate (e.g., each discrete state ofthe respiratory cycle) is reconstructed from the elongated motionvectors and/or mu-maps, the derived gate norm, and a plurality ofsecond-modality data for each gate. The plurality of second-modalitydata may comprise, for example, one or more sinograms. A sinogramcomprises annihilation event data acquired over a sub-period of a gate.For example, each gate may comprise one or more sinograms capturedduring consecutive time periods. In some embodiments, the secondmodality data comprises PET sinogram data. In a sixth step 312, motioncorrection is applied to each reconstructed gate to compensate formovement of the patient. Motion correction may be applied by utilizingan inverse motion vector to compensate for motion, such as respiratorymotion, during the gate period (see Equation 2 below). In someembodiments, depending on the amplitude of the motion vectors and/or thesize of the reconstructed FOV along the z-axis, some of the voxels atthe edge of the planes could lie outside the single-bed FOV of thesecond imaging modality. To improve the signal to noise ratio (SNR) ofthe final motion corrected reconstructed image, the individual frames ofeach bed are warped to a reference frame and are summed together. The 3Dspatial motion warping operation from frame n to frame m can be denotedas:f _(m,b) ^(mc) =T _(m,n,b)( x )·f _(n,b)( x )  (Equation 1)where f(x) is a discretized version of the time varying 3D image forframe m and bed b while x is the center of the j^(th) voxel (j=1 . . .J), T(x) is a warping function from frame n to frame m, and M is thetotal number of frames summed for a specific bed. In some embodiments,one or more of the terms in the 3D spatial motion warping operation, theattenuation data, and/or the measured data may be larger than a scannerFOV of one or more of the imaging modalities 112, 114 and/or a desiredreconstructed FOV. By using a larger FOV than the scanner FOV and/or thedesired reconstructed FOV, the 3D spatial warping operation ensures thatno truncation artifacts are generated at the edges of the reconstructedgated images. In some embodiments, an expanded PET FOV and an expandedsensitivity term are generated from adjacent bed motion vectors andsensitivity terms. The expanded PET FOV and sensitivity terms accountsfor variation in a motion pattern, such as, for example, a respiratorypattern, as well as variation in the sensitivity term between beds.

In a seventh step 314, after the each of the reconstructed gate imagesare motion corrected, each reconstructed frame is combined, or summed,to generate a single-bed image. In some embodiments, the individualframes are warped (e.g., motion corrected) and summed together in asingle step using post-reconstruction motion correction according to theequation:

$\begin{matrix}\frac{\sum\limits_{n = 1}^{M}\;\left( {{T_{m,n,b}\left( \overset{\_}{x} \right)}\left( {{f_{n,b}\left( \overset{\_}{x} \right)} \cdot {r_{n,b}\left( \overset{\_}{x} \right)} \cdot {d_{n,b}\left( \overset{\_}{x} \right)}} \right)} \right)}{\sum\limits_{n = 1}^{M}\;\left( \left( {{T_{m,n,b}\left( \overset{\_}{x} \right)}\left( {{r_{n,b}\left( \overset{\_}{x} \right)} \cdot {d_{n,b}\left( \overset{\_}{x} \right)}} \right)} \right) \right)} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$where r(x) is a sensitivity weighting term and d(x) is the frameduration. In some embodiments, the sensitivity term may be larger than ascanner FOV. Modeling of motion vectors using an enlarged sensitivityterm can provide a more accurate reconstruction.

In an eight step 316, the computer 130 checks if any beds remain to beprocessed. The second through seventh steps 304-314 of the method 300are repeated for each bed in the dual-modality data. For example, in oneembodiment, a first bed and a second bed are obtained by themulti-modality imaging apparatus 100. The computer 130 processes datacorresponding to the first bed to generate a single-bed image for thefirst bed according to steps two through seven 304-314 discussed above.The computer 130 processes the second bed to generate a single-bed imagefor the second bed according to steps two through seven 304-314.Although the method is discussed herein as processing each bedsequentially, it will be recognized that the computer 130 can processtwo or more of beds in serial and/or parallel.

FIG. 4A illustrates one embodiment of a bed-by-bed reconstructed image400 including six-beds 402 a-402 f. In the illustrated embodiment, thebed-by-bed reconstructed image is generated using an elongated mu-mapand motion vectors in conjunction with a modification to a 3Dsensitivity term to generate motion corrected PET images. Each bed 402a-402 f is generated by summing together a plurality of frames accordingto the method described above. As shown in FIG. 4A, in some embodiments,each bed includes an overlapping portion with the previous andsubsequent frames (if present). FIG. 4A illustrates a max diaphragmrespiratory motion of 2.5 cm and an anterior-posterior chest expansionof 1.2 cm having a respiratory cycle of 5 cm. Although specificparameters are shown herein, it will be recognized that the disclosedmethods are suitable for any amount of diaphragm and/oranterior-posterior motion over a respiratory cycle of any duration.

After all beds in the dual-modality data set have been processed, eachof the single-bed images are stitched, or summed, together to generate amulti-bed motion corrected full-body reconstruction in a ninth step 318.The motion corrected image from each bed is stitched together bymodeling the effects of motion in the 3D sensitivity map of each bed andframe. For example, in some embodiments, the full body motion correctedimage is generated according to the equation:

$\begin{matrix}{{f^{prmc}\left( \overset{\_}{x} \right)} = \frac{\sum\limits_{b = 1}^{B}\;{\sum\limits_{n = 1}^{M}\;\left( {{T_{m,n,b}\left( \overset{\_}{x} \right)}\left( {{f_{n,b}\left( \overset{\_}{x} \right)} \cdot {r_{n,b}\left( \overset{\_}{x} \right)} \cdot {d_{n,b}\left( \overset{\_}{x} \right)}} \right)} \right)}}{\sum\limits_{b = 1}^{B}\;{\sum\limits_{n = 1}^{M}\;\left( \left( {{T_{m,n,b}\left( \overset{\_}{x} \right)}\left( {{r_{n,b}\left( \overset{\_}{x} \right)} \cdot {d_{n,b}\left( \overset{\_}{x} \right)}} \right)} \right) \right)}}} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$The number of motion corrected frames m=1 . . . M_(b) and/or the scanduration of each frame d=1 . . . D_(m,b) in each bed can be independentof each other. Although the seventh step 314, the eight step 316, andthe ninth step 318 of the method 300 are discussed herein as discretesteps, it will be recognized that the steps 314-318 may be combined intoa single step, for example, utilizing the equation above. FIG. 4Billustrates one embodiment of a whole body stitched image 404 generatedfrom the six beds 402 a-402 f illustrated in FIG. 4A. The use ofelongated mu-maps and motion vectors of the first image modality (e.g.,MR-based mu-maps) eliminates attenuation mismatch at the edge of eachbed of the second imaging modality (e.g., PET data), for example causedby respiratory motion, which reduces the generation of artifacts in awhole body image.

FIG. 5A illustrates one embodiment of a non-uniform rational B-spline(NURBS)-based cardiac-torso (NCAT) phantom shown in ideal whole bodyimage 500 a. The NCAT phantom is used in evaluation to illustrate abest-case (or ground truth) and is used to detect any errors in areconstructed image. FIG. 5B illustrates one embodiment of a whole bodyreconstructed image 500 b generated using a single bed based motionfield. The use of a single bed based motion field in reconstruction cangenerate artifacts in the image. The whole body reconstructed image 500b was reconstructed without the use of an adjusted sensitivity termbased on adjacent motion vectors and without elongated mu-maps. As canbe seen in FIG. 5B, the whole body reconstructed image 500 b containsmultiple artifacts 502 a, 502 b, 502 c generated during thereconstruction process. The artifacts 502 a-502 c are generated due tomotion change between beds and attenuation of the edge voxels of thesecond imaging modality 114.

FIG. 5C illustrates one embodiment of a whole body reconstructed image500 c generated according to the present method. The whole bodyreconstructed image 500 c eliminates the artifacts shown FIG. 5B andprovides a whole body reconstructed image 500 c that is substantiallyidentical to the ideal NCAT phantom 500 a of FIG. 5A. As shown in FIG.5C, the methods disclosed herein produce a whole body reconstructedimage 500 c substantially free of defects. The use of an elongatedmu-map, motion vectors, and sensitivity terms allows the disclosedmethod to compensate for motion mismatch between multiple beds in thesecond modality data.

FIG. 6 illustrates one embodiment of a schematic representation of anMR-PET workflow 600 for generating a multi-bed elastic motion correctedwhole body image, such as, for example, the whole body reconstructedimage 500 c illustrated in FIG. 5C. As shown in FIG. 6, a mu-model scan602 is performed by an MR imaging modality 612. The MR imaging modality612 acquires one or more mu-maps 606 a-606 e with a FOV having a firstlength. The generated mu-maps 606 a-606 e are processed to generatemotion vector maps 608 a-608 e and are correlated with respiratorymotion phases. In some embodiments, a longer single mu-map is acquiredand one or more motion vectors are used to generate a series of mu-mapsthat correspond to each frame. After performing the mu-model scan 602,the MR imaging modality 612 captures diagnostic MR data 604 a, 604 b.Simultaneously with the acquisition of mu-model data 602 and diagnosticMR data 604 a, 604 b, a PET imaging modality 614 captures PET list-modedata 610. The PET list-mode data 610 is divided into a plurality of bins616 a-616 e corresponding to the respiratory phases identified by themu-maps 606 a-606 e. A plurality of single-frame PET images 618 a-618 eare generated by combining the PET list-mode data 614 with the MR-basedmu-maps 606 a-606 e. The single-frame PET images 618 a-618 e arecombined according to the method 300 illustrated in FIG. 3 to generatesingle-bed PET images and/or a whole body elastic motion corrected image620.

FIG. 7 is an architecture diagram of a computer system 700 that may beused in some embodiments, e.g., for implementing computer 130 shown inFIG. 1. Computer system 700 may include one or more processors 702. Eachprocessor 702 is connected to a communication infrastructure 706 (e.g.,a communications bus, cross-over bar, or network). Computer system 700may include a display interface 722 that forwards graphics, text, andother data from the communication infrastructure 706 (or from a framebuffer, not shown) for display on the display unit 724 to a user.

Computer system 700 may also include a main memory 704, such as a randomaccess memory (RAM), and a secondary memory 708. The main memory 704and/or the secondary memory 708 comprise non-transitory memory. Thesecondary memory 708 may include, for example, a hard disk drive (HDD)710 and/or removable storage drive 712, which may represent a floppydisk drive, a magnetic tape drive, an optical disk drive, a memorystick, or the like as is known in the art. The removable storage drive712 reads from and/or writes to a removable storage unit 716. Removablestorage unit 716 may be a floppy disk, magnetic tape, optical disk, orthe like. As will be understood, the removable storage unit 716 mayinclude a computer readable storage medium having tangibly storedtherein (embodied thereon) data and/or computer software instructions,e.g., for causing the processor(s) to perform various operations.

In alternative embodiments, secondary memory 708 may include othersimilar devices for allowing computer programs or other instructions tobe loaded into computer system 700. Secondary memory 708 may include aremovable storage unit 718 and a corresponding removable storageinterface 714, which may be similar to removable storage drive 712, withits own removable storage unit 716. Examples of such removable storageunits include, but are not limited to, USB or flash drives, which allowsoftware and data to be transferred from the removable storage unit 716,718 to computer system 700.

Computer system 700 may also include a communications interface (e.g.,networking interface) 720. Communications interface 720 allows softwareand data to be transferred between computer system 700 and externaldevices. Examples of communications interface 720 may include a modem,Ethernet card, wireless network card, a Personal Computer Memory CardInternational Association (PCMCIA) slot and card, or the like. Softwareand data transferred via communications interface 720 may be in the formof signals, which may be electronic, electromagnetic, optical, or thelike that are capable of being received by communications interface 720.These signals may be provided to communications interface 720 via acommunications path (e.g., channel), which may be implemented usingwire, cable, fiber optics, a telephone line, a cellular link, a radiofrequency (RF) link and other communication channels.

The apparatuses and processes are not limited to the specificembodiments described herein. In addition, components of each apparatusand each process can be practiced independent and separate from othercomponents and processes described herein.

The previous description of embodiments is provided to enable any personskilled in the art to practice the disclosure. The various modificationsto these embodiments will be readily apparent to those skilled in theart, and the generic principles defined herein may be applied to otherembodiments without the use of inventive faculty. The present disclosureis not intended to be limited to the embodiments shown herein, but is tobe accorded the widest scope consistent with the principles and novelfeatures disclosed herein.

What is claimed is:
 1. A method of processing data for medical imaging,the method comprising: providing a first set of first modality dataincluding a first mu-map, a first plurality of gated data, and a firstplurality of motion vectors, wherein the first set of first modalitydata is generated by a first imaging modality of an imaging system;providing a first set of second modality data including a firstplurality of frames, wherein each of the first plurality of framescorresponds to one of the first plurality of attenuation correctionmaps, and wherein the first set of second modality data is generated bya second imaging modality of an imaging system; warping the firstplurality of frames by one or more motion vectors from the firstplurality of motion vectors; combining the first plurality of warpedframes into a first single-bed image providing a second set of firstmodality data including a second mu-map, a second plurality of motionvectors and a second plurality of gated data; providing a second set ofsecond modality data including a second plurality of frames, whereineach of the second plurality of frames corresponds to one or more of thesecond plurality of motion vectors; warping the second plurality offrames by one or more motion vectors from the plurality of motionvectors; combining the second plurality of warped frames into asingle-bed image; and combining the first single-bed image and thesecond single-bed image into a whole body image.
 2. The method of claim1, wherein the first plurality of image frames comprise sinograms. 3.The method of claim 1, wherein generating the first single-bed image andthe second-single bed image comprises applying an expanded sensitivityterm and summing the respective plurality of warped frames.
 4. Themethod of claim 1, comprising: generating a first plurality ofattenuation maps from the first set of modality data, wherein each ofthe first plurality of attenuation maps corresponds to a gate in thefirst plurality of gated data; and warping the first plurality of framesby a corresponding attenuation map from the first plurality ofattenuation maps.
 5. The method of claim 4, wherein the first pluralityof attenuation correction maps are generated by warping an ungatedattenuation map by a plurality of elongated motion vectors correspondingto respiratory motion derived from the first set of first modality data.6. The method of claim 1, wherein the whole body image is generatedaccording to the equation:${f^{prmc}\left( \overset{\_}{x} \right)} = \frac{\sum\limits_{b = 1}^{B}\;{\sum\limits_{n = 1}^{M}\;\left( {{T_{m,n,b}\left( \overset{\_}{x} \right)}\left( {{f_{n,b}\left( \overset{\_}{x} \right)} \cdot {r_{n,b}\left( \overset{\_}{x} \right)} \cdot {d_{n,b}\left( \overset{\_}{x} \right)}} \right)} \right)}}{\sum\limits_{b = 1}^{B}\;{\sum\limits_{n = 1}^{M}\;\left( \left( {{T_{m,n,b}\left( \overset{\_}{x} \right)}\left( {{r_{n,b}\left( \overset{\_}{x} \right)} \cdot {d_{n,b}\left( \overset{\_}{x} \right)}} \right)} \right) \right)}}$where r(x) is the sensitivity weighting term, d(x) is a frame duration,m is the number of image frame, f(x) is a discretized version of frame mand bed b, x is a center of a j^(th) voxel (j=1 . . . J), T(x) is awarping function from frame n to frame m, and M is the total number offrames in the first plurality of frames and the second plurality offrames.
 7. The method of claim 1, wherein the first modality datacomprises one of magnetic resonance imaging modality data or computedtomography modality data.
 8. The method of claim 7, wherein the firstset of first modality data comprises a field of view greater than afield of view of the first set of second modality data.
 9. The method ofclaim 7, wherein the second modality data comprises positron emissiontomography modality data.
 10. A non-transitory, computer readable mediumstoring computer executable instructions which cause a computer toexecute the steps of: generating a first set of first modality dataincluding a first mu-map, a plurality of gated data, and a plurality ofmotion vectors, wherein the first set of first modality data isgenerated by a first imaging modality of an imaging system; generating afirst plurality of attenuation maps from the first set of first modalitydata, wherein each of the first plurality of attenuation mapscorresponds to a gate in the first plurality of gated data; generating afirst set of second modality data including a first plurality of frames,wherein each of the plurality of frames corresponds to one of theplurality of attenuation correction maps, and wherein the first set ofsecond modality data is generated by a second imaging modality of theimaging system; warping the first plurality of frames by one or morecorresponding motion vectors from the plurality of motion vectors and acorresponding attenuation map from the plurality of attenuation maps;generating a first single-bed image by combining the first plurality ofwarped frames generating a second set of first modality data including asecond mu-map, a second plurality of motion vectors, and a secondplurality of gated data; generating a second plurality of attenuationmaps from the second set of first modality data, wherein each of thesecond plurality of attenuation maps corresponds to a gate in the secondplurality of gated data; generating a second set of second modality dataincluding a second plurality of frames, wherein each of the secondplurality of frames corresponds to one of the second plurality ofattenuation correction maps; warping the second plurality of frames byone or more corresponding motion vectors from the plurality of motionvectors and a corresponding attenuation map from the plurality ofattenuation maps; generating a second single-bed image by combining thesecond plurality of warped frames; and generating a whole body image bycombining the first single-bed image and the second single-bed image.11. The non-transitory, computer readable medium of claim 10, whereingenerating each of the first and second single-bed images comprisesapplying a motion compensated sensitivity term and summing therespective plurality of image frames.
 12. The non-transitory, computerreadable medium of claim 11, wherein generating the whole body imagecomprises summing each of the single-bed images.
 13. The non-transitory,computer readable medium of claim 12, wherein the whole body image isgenerated according to the equation:${f^{prmc}\left( \overset{\_}{x} \right)} = \frac{\sum\limits_{b = 1}^{B}\;{\sum\limits_{n = 1}^{M}\;\left( {{T_{m,n,b}\left( \overset{\_}{x} \right)}\left( {{f_{n,b}\left( \overset{\_}{x} \right)} \cdot {r_{n,b}\left( \overset{\_}{x} \right)} \cdot {d_{n,b}\left( \overset{\_}{x} \right)}} \right)} \right)}}{\sum\limits_{b = 1}^{B}\;{\sum\limits_{n = 1}^{M}\;\left( \left( {{T_{m,n,b}\left( \overset{\_}{x} \right)}\left( {{r_{n,b}\left( \overset{\_}{x} \right)} \cdot {d_{n,b}\left( \overset{\_}{x} \right)}} \right)} \right) \right)}}$where r(x) is the sensitivity weighting term, d({circumflex over (x)})is a frame duration, m is the number of image frame, f(x) is adiscretized version of frame m and bed b, x is a center of a j^(th)voxel (j=1 . . . J), T(x) is a motion correction function, and M is thetotal number of frames in the first plurality of frames and the secondplurality of frames.
 14. The non-transitory, computer readable medium ofclaim 13, wherein the first imaging modality comprises one of a magneticresonance imaging modality or a computed tomography modality.
 15. Thenon-transitory, computer readable medium of claim 14, wherein the secondimaging modality comprises a positron emission tomography modality. 16.A system for medical imaging, comprising: a first imaging modality; asecond imaging modality; and a computer in data communication with thefirst imaging modality and the second imaging modality, the computerconfigured to process data for medical imaging by: generating a firstset of first modality data including a first mu-map, a plurality ofgated data, and a plurality of motion vectors, wherein the first set offirst modality data is generated by a first imaging modality of animaging system; generating a first set of second modality data includinga first plurality of frames, wherein each of the plurality of framescorresponds to one or more of the plurality of motion vectors, andwherein the first set of second modality data is generated by a secondimaging modality of the imaging system; warping the first plurality offrames by one or more corresponding motion vectors from the plurality ofmotion vectors; and generating a first single-bed image by combining thefirst plurality of warped frames generating a second set of firstmodality data including a second mu-map, a second plurality of motionvectors, and a second plurality of gated data; generating a secondplurality of attenuation maps from the second set of first modalitydata, wherein each of the second plurality of attenuation mapscorresponds to a gate in the second plurality of gated data; generatinga second set of second modality data including a second plurality offrames, wherein each of the second plurality of frames corresponds toone of the second plurality of attenuation correction maps; warping thesecond plurality of frames by one or more corresponding motion vectorsfrom the plurality of motion vectors and a corresponding attenuation mapfrom the plurality of attenuation maps; generating a second single-bedimage by combining the second plurality of warped frames; and generatinga whole body image by combining the first single-bed image and thesecond single-bed image.
 17. The system of claim 16, wherein the firstimaging modality comprises one of a magnetic resonance imaging modalityor a computed tomography modality and the second imaging modalitycomprises a positron emission topography modality.